| 1. |
- Abdella, Juhar Ahmed, et al.
(författare)
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Airline ticket price and demand prediction : A survey
- 2021
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Ingår i: Journal of King Saud University - Computer and Information Sciences. - : Elsevier. - 1319-1578. ; 33:4, s. 375-391
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays, airline ticket prices can vary dynamically and significantly for the same flight, even for nearby seats within the same cabin. Customers are seeking to get the lowest price while airlines are trying to keep their overall revenue as high as possible and maximize their profit. Airlines use various kinds of computational techniques to increase their revenue such as demand prediction and price discrimination. From the customer side, two kinds of models are proposed by different researchers to save money for customers: models that predict the optimal time to buy a ticket and models that predict the minimum ticket price. In this paper, we present a review of customer side and airlines side prediction models. Our review analysis shows that models on both sides rely on limited set of features such as historical ticket price data, ticket purchase date and departure date. Features extracted from external factors such as social media data and search engine query are not considered. Therefore, we introduce and discuss the concept of using social media data for ticket/demand prediction. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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| 2. |
- Alkharabsheh, Khalid, et al.
(författare)
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Prioritization of god class design smell : A multi-criteria based approach
- 2022
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Ingår i: JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES. - Amsterdam : Elsevier. - 1319-1578 .- 2213-1248. ; 34:10, s. 9332-9342
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Tidskriftsartikel (refereegranskat)abstract
- Context: Design smell Prioritization is a significant activity that tunes the process of software quality enhancement and raises its life cycle.Objective: A multi-criteria merge strategy for Design Smell prioritization is described. The strategy is exemplified with the case of God Class Design Smell.Method: An empirical adjustment of the strategy is performed using a dataset of 24 open source projects. Empirical evaluation was conducted in order to check how is the top ranked God Classes obtained by the proposed technique compared against the top ranked God class according to the opinion of developers involved in each of the projects in the dataset.Results: Results of the evaluation show the strategy should be improved. Analysis of the differences between projects where respondents answer correlates with the strategy and those projects where there is no correlation should be done.
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| 3. |
- Belgacem, Ali, et al.
(författare)
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Intelligent multi-agent reinforcement learning model for resources allocation in cloud computing
- 2022
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Ingår i: Journal of King Saud University - Computer and Information Sciences. - : Elsevier BV. - 1319-1578. ; 34:6, s. 2391-2404
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Tidskriftsartikel (refereegranskat)abstract
- Now more than ever, optimizing resource allocation in cloud computing is becoming more critical due to the growth of cloud computing consumers and meeting the computing demands of modern technology. Cloud infrastructures typically consist of heterogeneous servers, hosting multiple virtual machines with potentially different specifications, and volatile resource usage. This makes the resource allocation face many issues such as energy conservation, fault tolerance, workload balancing, etc. Finding a comprehensive solution that considers all these issues is one of the essential concerns of cloud service providers. This paper presents a new resource allocation model based on an intelligent multi-agent system and reinforcement learning method (IMARM). It combines the multi-agent characteristics and the Q-learning process to improve the performance of cloud resource allocation. IMARM uses the properties of multi-agent systems to dynamically allocate and release resources, thus responding well to changing consumer demands. Meanwhile, the reinforcement learning policy makes virtual machines move to the best state according to the current state environment. Also, we study the impact of IMARM on execution time. The experimental results showed that our proposed solution performs better than other comparable algorithms regarding energy consumption and fault tolerance, with reasonable load balancing and respectful execution time.
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| 4. |
- Chakir, Oumaima, et al.
(författare)
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An empirical assessment of ensemble methods and traditional machine for web-based attack detection in 5.0
- 2023
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Ingår i: JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES. - : ELSEVIER. - 1319-1578. ; 35:3, s. 103-119
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Tidskriftsartikel (refereegranskat)abstract
- Cybersecurity attacks that target software have become profitable and popular targets for cybercriminals who consciously take advantage of web-based vulnerabilities and execute attacks that might jeopardize essential industry 5.0 features. Several machine learning-based techniques have been developed in the literature to identify these types of assaults. In contrast to single classifiers, ensemble methods have not been evaluated empirically. To the best of our knowledge, this work is the first empirical evaluation of both homogeneous and heterogeneous ensemble approaches compared to single classifiers for web -based attack detection in industry 5.0, utilizing two of the most realistic public web-based attack data -sets. The authors divided the experiment into three main phases: In the first phase, they evaluated the performance of five well-established supervised machine learning (ML) classifiers. In the second phase, they constructed a heterogeneous ensemble of the three best-performing ML algorithms using max vot-ing and stacking methods. In the third phase, they used four well-known homogeneous ensembles to evaluate the performance of the bagging and boosting method. The results based on the ECML/PKDD 2007 and CSIC HTTP 2010 datasets revealed that bagging, particularly Random Forest, outperformed sin-gle classifiers in terms of accuracy, precision, F-value, FPR, and area of the ROC curve with values of 99.597%, 98.274%, 99.129%, 0.523%, 100 and 99.867%, 99.867%, 99.867%, 0.267%, 100, respectively. In con-trast, single classifiers performed better than boosting and stacking. However, in terms of FPR, the boost-ing exceeded single classifiers. Max voting is appropriate when accuracy, precision, and FPR are the primary concerns, whereas single classifiers can be employed when recall, FNR, training, and prediction times are critical elements. In terms of training time, ensemble approaches are more likely to be affected by data volume than single classifiers. The papers findings will help security researchers and practition-ers identify the most efficient learning techniques for securing web applications. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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| 5. |
- Wang, Junyong, et al.
(författare)
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Automatic mapping of configuration options in software using static analysis
- 2022
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Ingår i: Journal of King Saud University - Computer and Information Sciences. - : Elsevier. - 1319-1578 .- 2213-1248. ; 10:part B, s. 10044-10055
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Tidskriftsartikel (refereegranskat)abstract
- Configuration errors are some of the main reasons for software failures. Some configuration options may even negatively impact the software’s security, so that if a user sets the options inappropriately, there may be a huge security risk for the software. Recent studies have proposed mapping option read points to configuration options as the first step in alleviating the occurrence of configuration errors. Sadly, most available techniques use humans, and the rest require additional input, like an operation manual. Unfortunately, not all software is standardized and friendly. We propose a technique based on program and static analysis that can automatically map all the configuration options of a program just by reading the source code. Our evaluation shows that this technique achieves 88.6%, 97.7%, 94.6%, 94.8%, and 92.6% success rates with the Hadoop modules Common, Hadoop distributed file system, MapReduce, and YARN, and also PX4, when extracting configuration options. We found 53 configuration options in PX4 that were not documented and submitted these to the developers. Compared with published work, our technique is more effective in mapping options, and it may lay the foundation for subsequent research on software configuration security.
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